基于深度学习的 PLC 系统信道估计

IF 1.8 4区 计算机科学 Q3 TELECOMMUNICATIONS Annals of Telecommunications Pub Date : 2024-07-20 DOI:10.1007/s12243-024-01051-3
Nasser Sadeghi, Masoumeh Azghani
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引用次数: 0

摘要

电力线通信系统(PLC)用于数据传输。准确的信道状态信息(CSI)对此类系统的接收器设计至关重要,然而,脉冲噪声给信道估计任务带来了挑战。在本文中,我们提出了一种基于深度学习的 PLC 信道估计方法,该方法可抵御脉冲噪声和加性白高斯噪声(AWGN)。所提出的深度神经网络由三个子网络组成:第一个是去噪网络,旨在去除接收信号中的噪声。第二个子网络利用去噪信号对信道进行低精度估计。第三个子网络是为高精度信道估计而设计的。拟议网络的训练分两个阶段进行:首先,训练去噪子网络。其次,通过冻结去噪网络的训练参数,训练双信道估计子网络。此外,我们还推导出了 PLC 信道估计问题的 Cramer Rao 下界。我们通过各种模拟场景对所提出的方法进行了评估,结果证实该方法优于其他方法。建议的算法对脉冲噪声和高斯噪声具有可接受的抵抗力。
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Deep learning based channel estimation in PLC systems

Power line communication systems (PLC) are used for data transmission. Accurate channel state information (CSI) is essential for the receiver design in such systems, however, impulsive noise poses a challenge for the channel estimation task. In this paper, we propose a deep learning based method for PLC channel estimation which is resistant against impulsive noise as well as the additive white Gaussian noise (AWGN). The proposed deep neural network consists of three sub-networks: The first one is a denoising network which aims to remove the noise from the received signal. The second sub-network offers a low-accuracy estimation of the channel using the denoised signal. The third sub-network is designed for high-accuracy channel estimation. The training of the proposed network is done in two stages: Firstly, the denoising sub-network is trained. Secondly, by freezing the trained parameters of the denoising network, the two-channel estimation sub-networks are trained. Moreover, we have derived the Cramer Rao lower bound of the PLC channel estimation problem. The proposed method has been evaluated through various simulation scenarios which confirm the superiority of the proposed method over its counterpart. The suggested algorithm indicates acceptable resistance against impulsive and Gaussian noises.

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来源期刊
Annals of Telecommunications
Annals of Telecommunications 工程技术-电信学
CiteScore
5.20
自引率
5.30%
发文量
37
审稿时长
4.5 months
期刊介绍: Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.
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